Density-Preserving Sampling: Robust and Efficient Alternative to Cross-Validation for Error Estimation
نویسندگان
چکیده
منابع مشابه
Efficient Algorithms for Minimizing Cross Validation Error
Model selection is important in many areas of supervised learning. Given a dataset and a set of models for predicting with that dataset, we must choose the model which is expected to best predict future data. In some situations, such as online learning for control of robots or factories, data is cheap and human expertise costly. Cross validation can then be a highly effective method for automat...
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ژورنال
عنوان ژورنال: IEEE Transactions on Neural Networks and Learning Systems
سال: 2013
ISSN: 2162-237X,2162-2388
DOI: 10.1109/tnnls.2012.2222925